Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations4000
Missing cells0
Missing cells (%)0.0%
Duplicate rows17
Duplicate rows (%)0.4%
Total size in memory772.4 KiB
Average record size in memory197.7 B

Variable types

Numeric16
Categorical2

Alerts

Dataset has 17 (0.4%) duplicate rowsDuplicates
camcol is highly overall correlated with decHigh correlation
dec is highly overall correlated with camcol and 1 other fieldsHigh correlation
field is highly overall correlated with raHigh correlation
g is highly overall correlated with i and 3 other fieldsHigh correlation
i is highly overall correlated with g and 3 other fieldsHigh correlation
objid is highly overall correlated with dec and 1 other fieldsHigh correlation
r is highly overall correlated with g and 3 other fieldsHigh correlation
ra is highly overall correlated with fieldHigh correlation
run is highly overall correlated with objidHigh correlation
u is highly overall correlated with g and 3 other fieldsHigh correlation
z is highly overall correlated with g and 3 other fieldsHigh correlation
clean is highly imbalanced (51.7%) Imbalance

Reproduction

Analysis started2025-02-16 00:25:03.519436
Analysis finished2025-02-16 00:26:20.805226
Duration1 minute and 17.29 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

objid
Real number (ℝ)

High correlation 

Distinct3983
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2376487 × 1018
Minimum1.2376464 × 1018
Maximum1.2376499 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:20.974768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.2376464 × 1018
5-th percentile1.2376487 × 1018
Q11.2376487 × 1018
median1.2376487 × 1018
Q31.2376487 × 1018
95-th percentile1.2376487 × 1018
Maximum1.2376499 × 1018
Range3.5368888 × 1012
Interquartile range (IQR)1.717017 × 1010

Descriptive statistics

Standard deviation4.2442219 × 1011
Coefficient of variation (CV)3.4292623 × 10-7
Kurtosis21.348355
Mean1.2376487 × 1018
Median Absolute Deviation (MAD)2.6682002 × 109
Skewness-3.8425877
Sum6.8672649 × 1018
Variance1.801342 × 1023
MonotonicityNot monotonic
2025-02-15T19:26:21.243709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237648721 × 10182
 
0.1%
1.237648722 × 10182
 
0.1%
1.23764872 × 10182
 
0.1%
1.237648706 × 10182
 
0.1%
1.237648721 × 10182
 
0.1%
1.237648721 × 10182
 
0.1%
1.237648723 × 10182
 
0.1%
1.23764872 × 10182
 
0.1%
1.23764872 × 10182
 
0.1%
1.237648722 × 10182
 
0.1%
Other values (3973) 3980
99.5%
ValueCountFrequency (%)
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
ValueCountFrequency (%)
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%
1.237649918 × 10181
< 0.1%

ra
Real number (ℝ)

High correlation 

Distinct3983
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean185.52329
Minimum10.469743
Maximum249.75831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:21.628294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.469743
5-th percentile119.28463
Q1162.47467
median195.81545
Q3214.34843
95-th percentile234.86906
Maximum249.75831
Range239.28857
Interquartile range (IQR)51.87376

Descriptive statistics

Standard deviation41.689446
Coefficient of variation (CV)0.22471273
Kurtosis3.9209051
Mean185.52329
Median Absolute Deviation (MAD)24.717197
Skewness-1.6050074
Sum742093.18
Variance1738.0099
MonotonicityNot monotonic
2025-02-15T19:26:22.218773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
166.6230168 2
 
0.1%
154.0361592 2
 
0.1%
202.1356181 2
 
0.1%
247.8968214 2
 
0.1%
229.9321432 2
 
0.1%
182.0159398 2
 
0.1%
169.6234048 2
 
0.1%
188.1115672 2
 
0.1%
195.6881045 2
 
0.1%
230.6643759 2
 
0.1%
Other values (3973) 3980
99.5%
ValueCountFrequency (%)
10.46974318 1
< 0.1%
10.56454003 1
< 0.1%
10.64180442 1
< 0.1%
10.66544908 1
< 0.1%
10.6720908 1
< 0.1%
10.7652692 1
< 0.1%
10.77358507 1
< 0.1%
10.83909086 1
< 0.1%
10.94418571 1
< 0.1%
11.12317887 1
< 0.1%
ValueCountFrequency (%)
249.7583133 1
< 0.1%
249.7235743 1
< 0.1%
249.6161725 1
< 0.1%
249.501515 1
< 0.1%
249.5007148 1
< 0.1%
249.3834716 1
< 0.1%
249.3717383 1
< 0.1%
249.2177297 1
< 0.1%
249.1129862 1
< 0.1%
248.9820697 1
< 0.1%

dec
Real number (ℝ)

High correlation 

Distinct3983
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24895879
Minimum-1.2527631
Maximum13.85401
Zeros0
Zeros (%)0.0%
Negative1935
Negative (%)48.4%
Memory size31.4 KiB
2025-02-15T19:26:22.590439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2527631
5-th percentile-1.0919301
Q1-0.63182388
median0.053336256
Q30.59339562
95-th percentile1.1857498
Maximum13.85401
Range15.106773
Interquartile range (IQR)1.2252195

Descriptive statistics

Standard deviation2.0010746
Coefficient of variation (CV)8.0377747
Kurtosis35.399037
Mean0.24895879
Median Absolute Deviation (MAD)0.61955612
Skewness5.6856929
Sum995.83515
Variance4.0042997
MonotonicityNot monotonic
2025-02-15T19:26:22.952261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.546629818 2
 
0.1%
0.41793037 2
 
0.1%
-0.933404175 2
 
0.1%
0.990909226 2
 
0.1%
-0.607750181 2
 
0.1%
-0.116600409 2
 
0.1%
1.098641364 2
 
0.1%
-0.899130549 2
 
0.1%
-0.868581766 2
 
0.1%
0.229439206 2
 
0.1%
Other values (3973) 3980
99.5%
ValueCountFrequency (%)
-1.252763102 1
< 0.1%
-1.252320591 1
< 0.1%
-1.25191956 1
< 0.1%
-1.251031001 1
< 0.1%
-1.24936436 1
< 0.1%
-1.246636444 1
< 0.1%
-1.246573646 1
< 0.1%
-1.246031107 1
< 0.1%
-1.245091486 1
< 0.1%
-1.243987088 1
< 0.1%
ValueCountFrequency (%)
13.85401031 1
< 0.1%
13.85330013 1
< 0.1%
13.84939046 1
< 0.1%
13.84089199 1
< 0.1%
13.83275076 1
< 0.1%
13.81214842 1
< 0.1%
13.80296197 1
< 0.1%
13.79485176 1
< 0.1%
13.78859165 1
< 0.1%
13.78343991 1
< 0.1%

u
Real number (ℝ)

High correlation 

Distinct3952
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.609049
Minimum13.55178
Maximum19.59975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:23.252495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.55178
5-th percentile16.927199
Q118.170375
median18.843745
Q319.261563
95-th percentile19.532675
Maximum19.59975
Range6.04797
Interquartile range (IQR)1.0911875

Descriptive statistics

Standard deviation0.83943089
Coefficient of variation (CV)0.045108748
Kurtosis1.3468986
Mean18.609049
Median Absolute Deviation (MAD)0.499265
Skewness-1.2110905
Sum74436.195
Variance0.70464421
MonotonicityNot monotonic
2025-02-15T19:26:23.474578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.58449 2
 
0.1%
18.34448 2
 
0.1%
19.21814 2
 
0.1%
18.47695 2
 
0.1%
18.95048 2
 
0.1%
19.53721 2
 
0.1%
17.88213 2
 
0.1%
19.20444 2
 
0.1%
18.9664 2
 
0.1%
18.73745 2
 
0.1%
Other values (3942) 3980
99.5%
ValueCountFrequency (%)
13.55178 1
< 0.1%
14.27148 1
< 0.1%
15.19889 1
< 0.1%
15.24597 1
< 0.1%
15.2567 1
< 0.1%
15.30036 1
< 0.1%
15.36615 1
< 0.1%
15.44225 1
< 0.1%
15.50503 1
< 0.1%
15.61552 1
< 0.1%
ValueCountFrequency (%)
19.59975 1
< 0.1%
19.59929 1
< 0.1%
19.59895 1
< 0.1%
19.59887 1
< 0.1%
19.59867 1
< 0.1%
19.59834 1
< 0.1%
19.59801 1
< 0.1%
19.59785 1
< 0.1%
19.59779 1
< 0.1%
19.59761 1
< 0.1%

g
Real number (ℝ)

High correlation 

Distinct3953
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.363397
Minimum12.97487
Maximum22.49745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:23.708159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.97487
5-th percentile15.629518
Q116.79611
median17.472525
Q317.997818
95-th percentile18.815998
Maximum22.49745
Range9.52258
Interquartile range (IQR)1.2017075

Descriptive statistics

Standard deviation0.95096444
Coefficient of variation (CV)0.054768342
Kurtosis0.62153892
Mean17.363397
Median Absolute Deviation (MAD)0.5803
Skewness-0.43789411
Sum69453.586
Variance0.90433338
MonotonicityNot monotonic
2025-02-15T19:26:23.931780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.11525 2
 
0.1%
17.46911 2
 
0.1%
19.27966 2
 
0.1%
17.4726 2
 
0.1%
18.04827 2
 
0.1%
16.9155 2
 
0.1%
15.61154 2
 
0.1%
18.01077 2
 
0.1%
18.0898 2
 
0.1%
17.58908 2
 
0.1%
Other values (3943) 3980
99.5%
ValueCountFrequency (%)
12.97487 1
< 0.1%
13.42748 1
< 0.1%
13.78314 1
< 0.1%
13.99257 1
< 0.1%
14.05953 1
< 0.1%
14.10306 1
< 0.1%
14.11678 1
< 0.1%
14.14376 1
< 0.1%
14.16943 1
< 0.1%
14.2079 1
< 0.1%
ValueCountFrequency (%)
22.49745 1
< 0.1%
20.71108 1
< 0.1%
20.2118 1
< 0.1%
20.20427 1
< 0.1%
20.04382 1
< 0.1%
19.92393 1
< 0.1%
19.92153 1
< 0.1%
19.70171 1
< 0.1%
19.69143 1
< 0.1%
19.64128 1
< 0.1%

r
Real number (ℝ)

High correlation 

Distinct3963
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.839856
Minimum12.36285
Maximum22.26596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:24.213763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.36285
5-th percentile15.05854
Q116.190785
median16.850835
Q317.50664
95-th percentile18.675435
Maximum22.26596
Range9.90311
Interquartile range (IQR)1.315855

Descriptive statistics

Standard deviation1.0553432
Coefficient of variation (CV)0.062669374
Kurtosis0.43654361
Mean16.839856
Median Absolute Deviation (MAD)0.657815
Skewness-0.045367748
Sum67359.424
Variance1.1137493
MonotonicityNot monotonic
2025-02-15T19:26:24.675074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.20133 2
 
0.1%
17.50523 2
 
0.1%
17.09694 2
 
0.1%
17.38715 2
 
0.1%
17.62213 2
 
0.1%
15.03329 2
 
0.1%
16.48978 2
 
0.1%
15.4346 2
 
0.1%
17.08713 2
 
0.1%
17.29931 2
 
0.1%
Other values (3953) 3980
99.5%
ValueCountFrequency (%)
12.36285 1
< 0.1%
12.51399 1
< 0.1%
12.90478 1
< 0.1%
13.07748 1
< 0.1%
13.25364 1
< 0.1%
13.32631 1
< 0.1%
13.42119 1
< 0.1%
13.46431 1
< 0.1%
13.49015 1
< 0.1%
13.5704 1
< 0.1%
ValueCountFrequency (%)
22.26596 1
< 0.1%
20.34448 1
< 0.1%
20.3055 1
< 0.1%
20.11866 1
< 0.1%
20.07993 1
< 0.1%
19.99062 1
< 0.1%
19.77019 1
< 0.1%
19.65286 1
< 0.1%
19.62355 1
< 0.1%
19.60794 1
< 0.1%

i
Real number (ℝ)

High correlation 

Distinct3959
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.597712
Minimum12.01079
Maximum28.17963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:25.030200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.01079
5-th percentile14.786187
Q115.870782
median16.571195
Q317.258833
95-th percentile18.622701
Maximum28.17963
Range16.16884
Interquartile range (IQR)1.38805

Descriptive statistics

Standard deviation1.1412517
Coefficient of variation (CV)0.068759577
Kurtosis3.4108822
Mean16.597712
Median Absolute Deviation (MAD)0.69514
Skewness0.45678036
Sum66390.848
Variance1.3024554
MonotonicityNot monotonic
2025-02-15T19:26:25.266062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.73403 2
 
0.1%
16.04853 2
 
0.1%
17.50597 2
 
0.1%
15.46264 2
 
0.1%
17.42278 2
 
0.1%
15.83239 2
 
0.1%
15.53263 2
 
0.1%
16.32482 2
 
0.1%
16.88134 2
 
0.1%
16.15339 2
 
0.1%
Other values (3949) 3980
99.5%
ValueCountFrequency (%)
12.01079 1
< 0.1%
12.07615 1
< 0.1%
12.43369 1
< 0.1%
12.59386 1
< 0.1%
12.85262 1
< 0.1%
12.92891 1
< 0.1%
13.03327 1
< 0.1%
13.11638 1
< 0.1%
13.14345 1
< 0.1%
13.26782 1
< 0.1%
ValueCountFrequency (%)
28.17963 1
< 0.1%
24.35682 1
< 0.1%
21.92004 1
< 0.1%
20.66213 1
< 0.1%
20.62263 1
< 0.1%
20.59825 1
< 0.1%
20.29203 1
< 0.1%
20.27857 1
< 0.1%
20.09426 1
< 0.1%
20.0247 1
< 0.1%

z
Real number (ℝ)

High correlation 

Distinct3968
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.439392
Minimum11.79953
Maximum20.84769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:25.625281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.79953
5-th percentile14.569926
Q115.628205
median16.402665
Q317.147037
95-th percentile18.5865
Maximum20.84769
Range9.04816
Interquartile range (IQR)1.5188325

Descriptive statistics

Standard deviation1.1905021
Coefficient of variation (CV)0.072417647
Kurtosis0.37016676
Mean16.439392
Median Absolute Deviation (MAD)0.758975
Skewness0.21191619
Sum65757.568
Variance1.4172952
MonotonicityNot monotonic
2025-02-15T19:26:26.184999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.56668 2
 
0.1%
15.39552 2
 
0.1%
16.51886 2
 
0.1%
16.67511 2
 
0.1%
16.94267 2
 
0.1%
16.56421 2
 
0.1%
17.0636 2
 
0.1%
16.87079 2
 
0.1%
16.56451 2
 
0.1%
16.7088 2
 
0.1%
Other values (3958) 3980
99.5%
ValueCountFrequency (%)
11.79953 1
< 0.1%
12.1042 1
< 0.1%
12.20652 1
< 0.1%
12.25992 1
< 0.1%
12.55824 1
< 0.1%
12.6434 1
< 0.1%
12.76936 1
< 0.1%
12.81764 1
< 0.1%
12.87329 1
< 0.1%
13.00789 1
< 0.1%
ValueCountFrequency (%)
20.84769 1
< 0.1%
20.74779 1
< 0.1%
20.59599 1
< 0.1%
20.48277 1
< 0.1%
20.34913 1
< 0.1%
20.32314 1
< 0.1%
20.29061 1
< 0.1%
20.20325 1
< 0.1%
20.18666 1
< 0.1%
20.16837 1
< 0.1%

run
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean743.829
Minimum211
Maximum1035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:27.715795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum211
5-th percentile752
Q1752
median756
Q3756
95-th percentile756
Maximum1035
Range824
Interquartile range (IQR)4

Descriptive statistics

Standard deviation98.865936
Coefficient of variation (CV)0.13291487
Kurtosis21.343107
Mean743.829
Median Absolute Deviation (MAD)0
Skewness-3.8404459
Sum2975316
Variance9774.4734
MonotonicityNot monotonic
2025-02-15T19:26:28.584792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
756 2177
54.4%
752 1566
39.1%
211 85
 
2.1%
1035 77
 
1.9%
745 61
 
1.5%
259 33
 
0.8%
250 1
 
< 0.1%
ValueCountFrequency (%)
211 85
 
2.1%
250 1
 
< 0.1%
259 33
 
0.8%
745 61
 
1.5%
752 1566
39.1%
756 2177
54.4%
1035 77
 
1.9%
ValueCountFrequency (%)
1035 77
 
1.9%
756 2177
54.4%
752 1566
39.1%
745 61
 
1.5%
259 33
 
0.8%
250 1
 
< 0.1%
211 85
 
2.1%

camcol
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.37625
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:28.766273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6990902
Coefficient of variation (CV)0.50324776
Kurtosis-1.2765581
Mean3.37625
Median Absolute Deviation (MAD)2
Skewness0.0096306717
Sum13505
Variance2.8869077
MonotonicityNot monotonic
2025-02-15T19:26:28.931982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 800
20.0%
4 752
18.8%
5 716
17.9%
2 633
15.8%
3 581
14.5%
6 518
13.0%
ValueCountFrequency (%)
1 800
20.0%
2 633
15.8%
3 581
14.5%
4 752
18.8%
5 716
17.9%
6 518
13.0%
ValueCountFrequency (%)
6 518
13.0%
5 716
17.9%
4 752
18.8%
3 581
14.5%
2 633
15.8%
1 800
20.0%

field
Real number (ℝ)

High correlation 

Distinct663
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.602
Minimum19
Maximum812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:29.145847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile68.95
Q1311
median434
Q3527
95-th percentile732
Maximum812
Range793
Interquartile range (IQR)216

Descriptive statistics

Standard deviation167.16206
Coefficient of variation (CV)0.39838243
Kurtosis0.17722284
Mean419.602
Median Absolute Deviation (MAD)101
Skewness-0.143496
Sum1678408
Variance27943.155
MonotonicityNot monotonic
2025-02-15T19:26:29.381529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482 32
 
0.8%
365 30
 
0.8%
362 27
 
0.7%
368 27
 
0.7%
369 27
 
0.7%
301 25
 
0.6%
483 24
 
0.6%
375 23
 
0.6%
366 23
 
0.6%
363 23
 
0.6%
Other values (653) 3739
93.5%
ValueCountFrequency (%)
19 1
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
23 2
0.1%
24 2
0.1%
25 1
 
< 0.1%
26 3
0.1%
28 1
 
< 0.1%
30 2
0.1%
31 2
0.1%
ValueCountFrequency (%)
812 2
0.1%
811 3
0.1%
810 1
 
< 0.1%
809 2
0.1%
808 2
0.1%
806 4
0.1%
805 3
0.1%
804 3
0.1%
803 2
0.1%
802 2
0.1%

score
Real number (ℝ)

Distinct1563
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85176465
Minimum0.3019117
Maximum0.961145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:29.606563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.3019117
5-th percentile0.80245897
Q10.8368661
median0.8606745
Q30.8798336
95-th percentile0.911073
Maximum0.961145
Range0.6592333
Interquartile range (IQR)0.0429675

Descriptive statistics

Standard deviation0.071311212
Coefficient of variation (CV)0.083721733
Kurtosis38.477106
Mean0.85176465
Median Absolute Deviation (MAD)0.0211713
Skewness-5.6404382
Sum3407.0586
Variance0.005085289
MonotonicityNot monotonic
2025-02-15T19:26:29.827451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.836858 11
 
0.3%
0.8465437 11
 
0.3%
0.7824787 11
 
0.3%
0.9423295 11
 
0.3%
0.8799449 10
 
0.2%
0.8700646 10
 
0.2%
0.8328651 10
 
0.2%
0.8723025 10
 
0.2%
0.8568273 10
 
0.2%
0.9080893 10
 
0.2%
Other values (1553) 3896
97.4%
ValueCountFrequency (%)
0.3019117 1
 
< 0.1%
0.3024282 2
0.1%
0.3030093 1
 
< 0.1%
0.3041231 2
0.1%
0.3106202 1
 
< 0.1%
0.3132147 2
0.1%
0.3135445 1
 
< 0.1%
0.3145903 1
 
< 0.1%
0.3157648 3
0.1%
0.3178807 1
 
< 0.1%
ValueCountFrequency (%)
0.961145 6
0.1%
0.9566482 3
 
0.1%
0.9557393 3
 
0.1%
0.9540014 4
 
0.1%
0.953559 2
 
0.1%
0.9504662 3
 
0.1%
0.946646 2
 
0.1%
0.9423295 11
0.3%
0.9391892 1
 
< 0.1%
0.9386902 5
0.1%

clean
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size226.7 KiB
1
3582 
0
418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

Length

2025-02-15T19:26:30.082365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T19:26:30.259218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

Most occurring characters

ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3582
89.5%
0 418
 
10.4%

class
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size241.1 KiB
GALAXY
1857 
STAR
1629 
QSO
292 
S
 
177
G
 
44

Length

Max length6
Median length4
Mean length4.69025
Min length1

Characters and Unicode

Total characters18761
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSTAR
2nd rowGALAXY
3rd rowGALAXY
4th rowGALAXY
5th rowSTAR

Common Values

ValueCountFrequency (%)
GALAXY 1857
46.4%
STAR 1629
40.7%
QSO 292
 
7.3%
S 177
 
4.4%
G 44
 
1.1%
QUASAR 1
 
< 0.1%

Length

2025-02-15T19:26:30.510365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T19:26:30.986801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
galaxy 1857
46.4%
star 1629
40.7%
qso 292
 
7.3%
s 177
 
4.4%
g 44
 
1.1%
quasar 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 5345
28.5%
S 2099
 
11.2%
G 1901
 
10.1%
L 1857
 
9.9%
X 1857
 
9.9%
Y 1857
 
9.9%
R 1630
 
8.7%
T 1629
 
8.7%
Q 293
 
1.6%
O 292
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5345
28.5%
S 2099
 
11.2%
G 1901
 
10.1%
L 1857
 
9.9%
X 1857
 
9.9%
Y 1857
 
9.9%
R 1630
 
8.7%
T 1629
 
8.7%
Q 293
 
1.6%
O 292
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5345
28.5%
S 2099
 
11.2%
G 1901
 
10.1%
L 1857
 
9.9%
X 1857
 
9.9%
Y 1857
 
9.9%
R 1630
 
8.7%
T 1629
 
8.7%
Q 293
 
1.6%
O 292
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5345
28.5%
S 2099
 
11.2%
G 1901
 
10.1%
L 1857
 
9.9%
X 1857
 
9.9%
Y 1857
 
9.9%
R 1630
 
8.7%
T 1629
 
8.7%
Q 293
 
1.6%
O 292
 
1.6%

redshift
Real number (ℝ)

Distinct3932
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13401545
Minimum-0.003321956
Maximum4.192597
Zeros14
Zeros (%)0.4%
Negative648
Negative (%)16.2%
Memory size31.4 KiB
2025-02-15T19:26:32.014768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.003321956
5-th percentile-0.0002082177
Q10.0001061535
median0.0320384
Q30.08647908
95-th percentile0.9817242
Maximum4.192597
Range4.195919
Interquartile range (IQR)0.086372927

Descriptive statistics

Standard deviation0.37974182
Coefficient of variation (CV)2.8335674
Kurtosis22.051551
Mean0.13401545
Median Absolute Deviation (MAD)0.0321006
Skewness4.450826
Sum536.06181
Variance0.14420385
MonotonicityNot monotonic
2025-02-15T19:26:32.335953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.4%
-6.88 × 10-53
 
0.1%
-8.78 × 10-53
 
0.1%
0.1091683 2
 
0.1%
-1.02 × 10-52
 
0.1%
1.177334 × 10-52
 
0.1%
-8.08 × 10-52
 
0.1%
-7.74 × 10-52
 
0.1%
-2.56 × 10-52
 
0.1%
-9.23 × 10-52
 
0.1%
Other values (3922) 3966
99.2%
ValueCountFrequency (%)
-0.003321956 1
< 0.1%
-0.002372675 1
< 0.1%
-0.001026085 1
< 0.1%
-0.000979757 1
< 0.1%
-0.000964143 1
< 0.1%
-0.000912148 1
< 0.1%
-0.000858878 1
< 0.1%
-0.000848519 1
< 0.1%
-0.000837872 1
< 0.1%
-0.0008205 1
< 0.1%
ValueCountFrequency (%)
4.192597 1
< 0.1%
4.09963 1
< 0.1%
2.807709 1
< 0.1%
2.791397 1
< 0.1%
2.753311 1
< 0.1%
2.71233 1
< 0.1%
2.660608 1
< 0.1%
2.62735 1
< 0.1%
2.601201 1
< 0.1%
2.519756 1
< 0.1%

mjd
Real number (ℝ)

Distinct183
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52957.125
Minimum51608
Maximum58932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2025-02-15T19:26:32.637873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum51608
5-th percentile51613
Q151821
median51986
Q354468
95-th percentile56727
Maximum58932
Range7324
Interquartile range (IQR)2647

Descriptive statistics

Standard deviation1601.1481
Coefficient of variation (CV)0.030234801
Kurtosis0.014285792
Mean52957.125
Median Absolute Deviation (MAD)323
Skewness1.0736102
Sum2.118285 × 108
Variance2563675.3
MonotonicityNot monotonic
2025-02-15T19:26:32.953946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51984 132
 
3.3%
56727 119
 
3.0%
54970 110
 
2.8%
51609 110
 
2.8%
51990 102
 
2.5%
51689 92
 
2.3%
51671 90
 
2.2%
51615 87
 
2.2%
54567 87
 
2.2%
53816 85
 
2.1%
Other values (173) 2986
74.7%
ValueCountFrequency (%)
51608 20
 
0.5%
51609 110
2.8%
51612 34
 
0.9%
51613 55
1.4%
51614 16
 
0.4%
51615 87
2.2%
51616 1
 
< 0.1%
51630 18
 
0.4%
51633 25
 
0.6%
51637 41
 
1.0%
ValueCountFrequency (%)
58932 7
 
0.2%
58930 5
 
0.1%
58488 1
 
< 0.1%
58462 2
 
0.1%
56749 9
 
0.2%
56745 37
 
0.9%
56739 26
 
0.7%
56727 119
3.0%
56658 5
 
0.1%
56603 2
 
0.1%

rowv
Real number (ℝ)

Distinct3979
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00021804758
Minimum-0.2645156
Maximum0.1818706
Zeros2
Zeros (%)< 0.1%
Negative1765
Negative (%)44.1%
Memory size31.4 KiB
2025-02-15T19:26:33.212414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.2645156
5-th percentile-0.0064086733
Q1-0.0012119962
median0.000321998
Q30.0016976368
95-th percentile0.0070081944
Maximum0.1818706
Range0.4463862
Interquartile range (IQR)0.002909633

Descriptive statistics

Standard deviation0.0087940266
Coefficient of variation (CV)40.330768
Kurtosis296.8412
Mean0.00021804758
Median Absolute Deviation (MAD)0.0014609625
Skewness-5.6168567
Sum0.87219033
Variance7.7334903 × 10-5
MonotonicityNot monotonic
2025-02-15T19:26:33.601488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002149071 2
 
0.1%
0.008121597 2
 
0.1%
-0.003803096 2
 
0.1%
-0.00286046 2
 
0.1%
-3.04 × 10-52
 
0.1%
-0.001869018 2
 
0.1%
0.001003605 2
 
0.1%
0.000650717 2
 
0.1%
-0.000639366 2
 
0.1%
0.004916723 2
 
0.1%
Other values (3969) 3980
99.5%
ValueCountFrequency (%)
-0.2645156 1
< 0.1%
-0.1398193 1
< 0.1%
-0.1358781 1
< 0.1%
-0.06693275 1
< 0.1%
-0.06422039 1
< 0.1%
-0.054274 1
< 0.1%
-0.05404711 1
< 0.1%
-0.05160012 1
< 0.1%
-0.04396291 1
< 0.1%
-0.04297497 1
< 0.1%
ValueCountFrequency (%)
0.1818706 1
< 0.1%
0.1072399 1
< 0.1%
0.0934741 1
< 0.1%
0.08637648 1
< 0.1%
0.06073269 1
< 0.1%
0.06004669 1
< 0.1%
0.0550917 1
< 0.1%
0.04517849 1
< 0.1%
0.04484035 1
< 0.1%
0.04320135 1
< 0.1%

colv
Real number (ℝ)

Distinct3978
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.0040937 × 10-5
Minimum-0.1688072
Maximum0.1288143
Zeros2
Zeros (%)< 0.1%
Negative1920
Negative (%)48.0%
Memory size31.4 KiB
2025-02-15T19:26:34.134431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1688072
5-th percentile-0.0068975182
Q1-0.0016085975
median0.000125452
Q30.0016723462
95-th percentile0.0056239964
Maximum0.1288143
Range0.2976215
Interquartile range (IQR)0.0032809438

Descriptive statistics

Standard deviation0.007457006
Coefficient of variation (CV)-82.817951
Kurtosis127.64017
Mean-9.0040937 × 10-5
Median Absolute Deviation (MAD)0.001634329
Skewness-2.8478056
Sum-0.36016375
Variance5.5606938 × 10-5
MonotonicityNot monotonic
2025-02-15T19:26:34.579329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01249377 2
 
0.1%
0.001156267 2
 
0.1%
-0.000682925 2
 
0.1%
-0.001856848 2
 
0.1%
-0.009144525 2
 
0.1%
0.002610588 2
 
0.1%
0.000270429 2
 
0.1%
-0.000929943 2
 
0.1%
-0.001644898 2
 
0.1%
0.001375522 2
 
0.1%
Other values (3968) 3980
99.5%
ValueCountFrequency (%)
-0.1688072 1
< 0.1%
-0.1176926 1
< 0.1%
-0.1033961 1
< 0.1%
-0.05927097 1
< 0.1%
-0.05704488 1
< 0.1%
-0.052786 1
< 0.1%
-0.04888019 1
< 0.1%
-0.04573492 1
< 0.1%
-0.04379889 1
< 0.1%
-0.04273449 1
< 0.1%
ValueCountFrequency (%)
0.1288143 1
< 0.1%
0.07857239 1
< 0.1%
0.07477995 1
< 0.1%
0.06270446 1
< 0.1%
0.05491816 1
< 0.1%
0.05296343 1
< 0.1%
0.04758138 1
< 0.1%
0.04696766 1
< 0.1%
0.04605837 1
< 0.1%
0.04011283 1
< 0.1%

Interactions

2025-02-15T19:26:13.654139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:06.211900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:10.457204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:17.512203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:21.419007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:25.487735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:28.855936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:32.002504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:36.080963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:39.417960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:44.502435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:48.443145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:53.247773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:57.282202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:05.401324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:10.114289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:14.931685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:06.534948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:10.688958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:17.788888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:21.884503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:25.677683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:29.068626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-02-15T19:26:09.096938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:12.866729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:18.198693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:09.677798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:17.130501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:20.687805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:25.155461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:28.509098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:31.669527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:35.618160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:39.033012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:44.124773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:48.103588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:52.407651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:56.839891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:05.067014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:09.352538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:13.288478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:18.740137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:10.154546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:17.278388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:20.866241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:25.317762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:28.682866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:31.827694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:35.875463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:39.184602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:44.314512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:48.261374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:52.875329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:25:57.015829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:05.219596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:09.720731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-15T19:26:13.494737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-15T19:26:36.730833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
camcolclasscleancolvdecfieldgimjdobjidrraredshiftrowvrunscoreuz
camcol1.0000.0640.029-0.0700.8910.1680.0010.004-0.1220.2970.0020.141-0.019-0.036-0.1540.1250.012-0.001
class0.0641.0000.2340.0650.0170.0920.3020.2870.3310.1270.3030.1440.4180.1070.1270.0270.1280.305
clean0.0290.2341.0000.2920.0120.0670.0600.0890.1790.0000.0720.0900.0370.1970.0000.0000.0280.084
colv-0.0700.0650.2921.000-0.067-0.0010.0640.1320.1060.0150.110-0.019-0.0890.1010.0520.001-0.0440.151
dec0.8910.0170.012-0.0671.0000.0590.003-0.002-0.1240.500-0.001-0.026-0.023-0.0230.0860.0590.015-0.008
field0.1680.0920.067-0.0010.0591.0000.0780.067-0.163-0.1140.0760.8400.0390.007-0.223-0.0140.0450.061
g0.0010.3020.0600.0640.0030.0781.0000.911-0.1130.0370.9580.0970.3560.0510.0250.0380.8130.870
i0.0040.2870.0890.132-0.0020.0670.9111.0000.0100.0160.9850.0750.1360.0910.0080.0480.5820.991
mjd-0.1220.3310.1790.106-0.124-0.163-0.1130.0101.0000.007-0.045-0.240-0.4410.0180.0840.035-0.1680.050
objid0.2970.1270.0000.0150.500-0.1140.0370.0160.0071.0000.026-0.3680.0610.0230.8820.0620.0380.013
r0.0020.3030.0720.110-0.0010.0760.9580.985-0.0450.0261.0000.0900.2250.0810.0170.0450.6620.968
ra0.1410.1440.090-0.019-0.0260.8400.0970.075-0.240-0.3680.0901.0000.077-0.009-0.4960.0590.0620.067
redshift-0.0190.4180.037-0.089-0.0230.0390.3560.136-0.4410.0610.2250.0771.000-0.0420.0510.0170.3820.071
rowv-0.0360.1070.1970.101-0.0230.0070.0510.0910.0180.0230.081-0.009-0.0421.0000.0430.020-0.0300.102
run-0.1540.1270.0000.0520.086-0.2230.0250.0080.0840.8820.017-0.4960.0510.0431.000-0.0190.0270.009
score0.1250.0270.0000.0010.059-0.0140.0380.0480.0350.0620.0450.0590.0170.020-0.0191.000-0.0040.047
u0.0120.1280.028-0.0440.0150.0450.8130.582-0.1680.0380.6620.0620.382-0.0300.027-0.0041.0000.518
z-0.0010.3050.0840.151-0.0080.0610.8700.9910.0500.0130.9680.0670.0710.1020.0090.0470.5181.000

Missing values

2025-02-15T19:26:19.691382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-15T19:26:20.595257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

objidradecugrizruncamcolfieldscorecleanclassredshiftmjdrowvcolv
01237648722306924571185.5744860.70140219.1103417.6209917.0346416.8299316.7171175654660.8641451STAR8.780529e-05541400.0024180.001363
11237648720167436418203.801841-0.85390618.9095717.6294017.0146516.6205916.3523975615880.8165411GALAXY8.619995e-02519550.0086500.007056
21237648720685105352160.034264-0.42162619.2634017.6769316.8255116.4230716.1386475622950.8703811GALAXY1.330486e-01519130.003938-0.002028
31237648703510937836200.807373-0.68202418.8202217.7776517.3857517.1112816.9354075223820.8678011GALAXY8.636355e-02519590.0018390.001160
41237648720161275992189.792404-1.00148018.9139119.0535519.4627819.7566020.0961375614940.8338241STAR-4.090000e-08545620.001929-0.000465
51237648720132178408123.386052-1.04549918.5221417.4517317.2311217.1642717.132147561500.8514171STAR1.190440e-0553816-0.002165-0.000364
61237648720165863598200.243728-0.89593118.0952316.8276416.1894215.8350315.5867175615640.8152950GALAXY4.815651e-0251984-0.0116100.002361
71237648704054624442216.256661-0.22079919.2076518.7317318.8430218.9835319.1512875234860.8040861STAR-2.234210e-0451613-0.002153-0.001026
81237648722308890870190.0508470.69567217.8295115.9009115.0251514.6188114.3035375654960.8228981GALAXY4.634102e-0251941-0.000474-0.001548
91237648720163963081195.951990-0.96600517.7613216.5132715.8981115.5306915.3123675615350.8113361GALAXY8.944633e-02519860.0008940.001024
objidradecugrizruncamcolfieldscorecleanclassredshiftmjdrowvcolv
3990123764638207980398695.2261260.95725119.0661317.3429316.7224516.4307416.2217821163620.8162451STAR0.000072529310.000449-0.000302
39911237648722832982102160.9664851.06169519.2647217.1576116.1111015.6609315.3098675663010.8524921GALAXY0.115905519100.0024220.001939
3992123764638154286724595.0026280.43915518.9694417.3983417.0969216.9792716.8617221153610.8371281S0.00024952931-0.0001720.000898
39931237648721753080096146.8877420.21745419.5212117.7441016.8867116.4061216.0294875642070.8509501GALAXY0.062968516300.001537-0.006798
39941237648702972493932197.088527-1.21883318.4452016.9076416.2243215.8803615.6240475213580.8222361GALAXY0.11786151986-0.0008240.000152
39951237648720690741273172.826005-0.48430318.8189017.8927317.5437217.4136317.3182175623810.8878031STAR0.000152548910.0006750.000324
3996123764991842726708611.16512613.74524017.4666515.9614215.3601415.1457215.0445310351380.8448261STAR0.00004253242-0.000440-0.000301
39971237648720164684046197.579529-0.97157319.4568218.3255517.8691617.5751217.4164875615460.8191511GALAXY0.081606519850.0113480.005854
39981237648721206575779124.877626-0.04811019.2015817.3145016.3657315.9176015.577367563600.8940441GALAXY0.08744855888-0.004486-0.006072
39991237648720161800205190.920230-0.97253217.6372516.6554416.2997016.1473316.0954475615020.8451731STAR0.000213519280.0013720.000094

Duplicate rows

Most frequently occurring

objidradecugrizruncamcolfieldscorecleanclassredshiftmjdrowvcolv# duplicates
0123764638207960686994.6794990.84232118.4769516.9489716.6812616.5988716.5174721163590.8062041STAR0.000207529310.000372-0.0010172
1123764638207967264294.8838870.99068617.0384715.4722415.1029714.9705114.8829721163600.8069831S0.00001252931-0.0014830.0013762
21237648703513428079206.374167-0.78167318.2810117.1885616.7017516.3317316.1462675224200.9047681GALAXY0.08813651943-0.0028600.0011562
31237648705132888168226.6218280.59532918.9636318.6763718.4723418.4194917.9654775255550.8714171QSO0.369813519900.002106-0.0001102
41237648705134461016230.2193740.56074119.4273319.2796619.5904819.8685220.0857175255790.8759171STAR-0.00011255327-0.0000590.0000942
51237648705679065271247.8968210.99090916.7465915.6175215.4346015.4019615.4079475266970.8427831STAR0.000073516710.002149-0.0009302
61237648720160555035188.111567-0.89913117.2234815.6115415.0332914.8286514.7266475614830.8572180STAR0.00008454567-0.0018690.0026112
71237648720163832016195.688105-0.86858219.2044418.0107717.3257516.9054316.6704175615330.8032851GALAXY0.109168519860.0006510.0031562
81237648720166715396202.135618-0.93340418.6803617.1608316.4977016.1533915.9508975615770.8049311GALAXY0.054868519590.0024690.0010232
91237648720687988788166.623017-0.54663019.5552817.7392816.8670316.4481016.1176175623390.8790961GALAXY0.086721519000.001004-0.0018572